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Due to the complexity of scientific experiments, existing methods for capturing the provenance information from the scientific workflows (SWFs) face high programming overhead and code errors. In addition, the coarse-grained nature of the SWF provenance implies the loss of internal details of the workflow processes, which can lead to incomplete or inaccurate data dependencies and dependency differentiation problems. The diversity in scientific fields also reveals the limited versatility of the SWF provenance models. In this context, we propose a content-rich and fine-grained SWF provenance model (CF-PROV). This model provides normative transformations and documentation declarations for the multi-field SWFs, reducing the programming overhead and increasing the versatility. Our method of dividing the workflow provenance into data provenance and process provenance and our formal description of data deduction at the field level enrich the provenance information and transform the coarse-grained workflow provenance into a fine-grained provenance. Finally, the experiments on the model compression ratio and model generation time in multiple scientific fields demonstrate the versatility and rationality of the CF-PROV.

CF-PROV: A Content-Rich and Fine-Grained Scientific Workflow Provenance Model